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Multivariate Bernstein-type Polynomials of Finitely Generated D-Modules
Alexander Levin
https://arxiv.org/abs/2404.02069 https://arxi…

Multivariate Bernstein-type Polynomials of Finitely Generated D-Modules
We generalize the Gröbner basis method for free D-modules to the case of several term orderings induced by a partition of the set of basic variables. Using this generalized Gröbner basis technique we prove the existence and give a method of computation of a dimension polynomial in several variables associated with a finitely generated D-module. We show that this dimension polynomial carries more invariants of a D-module than the classical (univariate) Bernstein polynomial. We also show that t…

VIXEN: Visual Text Comparison Network for Image Difference Captioning
Alexander Black, Jing Shi, Yifei Fai, Tu Bui, John Collomosse
https://arxiv.org/abs/2402.19119

VIXEN: Visual Text Comparison Network for Image Difference Captioning
We present VIXEN - a technique that succinctly summarizes in text the visual differences between a pair of images in order to highlight any content manipulation present. Our proposed network linearly maps image features in a pairwise manner, constructing a soft prompt for a pretrained large language model. We address the challenge of low volume of training data and lack of manipulation variety in existing image difference captioning (IDC) datasets by training on synthetically manipulated images…

Self-supervised learning for classifying paranasal anomalies in the maxillary sinus
Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Lennart Maack, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer
https://arxiv.org/abs/2404.18599 https://arxiv.org/pdf/2404.18599
arXiv:2404.18599v1 Announce Type: new
Abstract: Purpose: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised learning methods require large labelled dataset exhibiting diverse anomaly morphology. Self-supervised learning (SSL) can be used to learn representations from unlabelled data. However, there are no SSL methods designed for the downstream task of classifying paranasal anomalies in the maxillary sinus (MS).
Methods: Our approach uses a 3D Convolutional Autoencoder (CAE) trained in an unsupervised anomaly detection (UAD) framework. Initially, we train the 3D CAE to reduce reconstruction errors when reconstructing normal maxillary sinus (MS) image. Then, this CAE is applied to an unlabelled dataset to generate coarse anomaly locations by creating residual MS images. Following this, a 3D Convolutional Neural Network (CNN) reconstructs these residual images, which forms our SSL task. Lastly, we fine-tune the encoder part of the 3D CNN on a labelled dataset of normal and anomalous MS images.
Results: The proposed SSL technique exhibits superior performance compared to existing generic self-supervised methods, especially in scenarios with limited annotated data. When trained on just 10% of the annotated dataset, our method achieves an Area Under the Precision-Recall Curve (AUPRC) of 0.79 for the downstream classification task. This performance surpasses other methods, with BYOL attaining an AUPRC of 0.75, SimSiam at 0.74, SimCLR at 0.73 and Masked Autoencoding using SparK at 0.75.
Conclusion: A self-supervised learning approach that inherently focuses on localizing paranasal anomalies proves to be advantageous, particularly when the subsequent task involves differentiating normal from anomalous maxillary sinuses. Access our code at https://github.com/mtec-tuhh/self-supervised-paranasal-anomaly

Towards an Enforceable GDPR Specification
Fran\c{c}ois Hublet, Alexander Kvamme, Sr{\dj}an Krsti\'c
https://arxiv.org/abs/2402.17350 https://

Towards an Enforceable GDPR Specification
While Privacy by Design (PbD) is prescribed by modern privacy regulations such as the EU's GDPR, achieving PbD in real software systems is a notoriously difficult task. One emerging technique to realize PbD is Runtime enforcement (RE), in which an enforcer, loaded with a specification of a system's privacy requirements, observes the actions performed by the system and instructs it to perform actions that will ensure compliance with these requirements at all times. To be able to use RE technique…

Cyclotron line evolution revealed with pulse-to-pulse analysis in the 2020 outburst of 1A 0535 262
Shui Qingcang, Zhang Shu, Wang Pengju, Mushtukov Alexander, Santangelo Andrea, Zhang Shuangnan, Kong Lingda, Ji Long, Chen Yupeng, Doroshenko Victor, Frontera Fillipo, Chang Zhi, Peng Jingqiang, Yin Hongxing, Qu Jinlu, Tao Lian, Ge Mingyu, Li Jian, Ye Wentao, Li Panping

Cyclotron line evolution revealed with pulse-to-pulse analysis in the 2020 outburst of 1A 0535+262
We present a detailed analysis of the X-ray luminosity (Lx) dependence of the cyclotron absorption line energy (Ecyc) for the X-ray binary pulsar 1A 0535+262 during its 2020 giant outburst based on pulse-to-pulse analysis. By applying this technique to high cadence observations of Insight-HXMT, we reveal the most comprehensive Ecyc-Lx correlation across a broad luminosity range of ~(0.03-1.3)*10^38 erg/s. Apart from the positive and negative correlations between cyclotron line energy and lumino…

A Hybrid Process for Integration of Organic Electrochemical Transistors for High Uniformity & Reliability
Tommy Meier, Yeohoon Yoon, Laura Teuerle, Ali Solgi, Karl Leo, Hans Kleemann
https://arxiv.org/abs/2404.12806

A Hybrid Process for Integration of Organic Electrochemical Transistors for High Uniformity & Reliability
Photolithography is believed to be a complementary technique to large-area printing, allowing for nanometer-scale integration and offering cost-efficiency. For organic electronics though, adapting photolithography is very challenging due to chemical incompatibilities. However, with the help of Alexander Zakhidov, orthogonal resins opened up the prospect of adapting the well-established process of photolithography for organic electronics. Here, we present a hybrid fabrication method for organic …

Cyclotron line evolution revealed with pulse-to-pulse analysis in the 2020 outburst of 1A 0535 262
Shui Qingcang, Zhang Shu, Wang Pengju, Mushtukov Alexander, Santangelo Andrea, Zhang Shuangnan, Kong Lingda, Ji Long, Chen Yupeng, Doroshenko Victor, Frontera Fillipo, Chang Zhi, Peng Jingqiang, Yin Hongxing, Qu Jinlu, Tao Lian, Ge Mingyu, Li Jian, Ye Wentao, Li Panping

Cyclotron line evolution revealed with pulse-to-pulse analysis in the 2020 outburst of 1A 0535+262
We present a detailed analysis of the X-ray luminosity (Lx) dependence of the cyclotron absorption line energy (Ecyc) for the X-ray binary pulsar 1A 0535+262 during its 2020 giant outburst based on pulse-to-pulse analysis. By applying this technique to high cadence observations of Insight-HXMT, we reveal the most comprehensive Ecyc-Lx correlation across a broad luminosity range of ~(0.03-1.3)*10^38 erg/s. Apart from the positive and negative correlations between cyclotron line energy and lumino…

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, Elie Akanny, Christina Kohlmann, Alexander Mitsos
https://arxiv.org/abs/2403.03767

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in industry. Recently, classical QSPR and Graph Neural Networks (GNNs), a deep learning technique, have been successfully applied to predict the CMC of surfactants at room temperature. However, these models have not yet considered the temperature dependency of the CMC, which is highly relevant for practical applications. We herein develop a GNN model for temperature-dependent CM…